One of the biggest disruptors these days, besides artificial intelligence is blockchain technology and its pioneer cryptocurrency, bitcoin. Many people believe that bitcoin and other cryptocurrencies, will help in making most sustainable and transparent economy. But with its fluctuations in price are constantly bringing out the question: “Should I invest?”. We want to help in understanding price fluctuation, predict the future movement and quantify risks of potential investments as well as potential profits. Predicting exact price in next moment is hard and maybe impossible, but using machine learning to predict some change of price with some risks or finding out which time range is appropriate for buying or selling is something that isn’t impossible and it could be useful as input to people who are trading or to investors.

2. Bitcoin Trade Signals

3. Content:
● Motivation
● Twitter Influencers Analysis
● Historical Data Analysis
● Trader
● Results and Future Work

4. ● Why Bitcoin? ● Why price prediction?

5. Twitter Influencers Analysis
Training and Testing
● SVM
● Ensemble Models
○ AdaBoost
○ XGB
○ Gradient Boost
Collecting Data
● Identifying the influencers
● Collecting relevant tweets
● Cleaning data
Feature Engineering and
Labelling
● Sentiment Analysis
● Financial Predictors
● Trend Labels

6. Historical Data Analysis
Training and Testing
● SVM
● Ensemble Models
○ AdaBoost
○ XGB
○ Gradient Boost
Collecting Data
● Identifying the appropriate
sources of relevant
blockchain and bitcoin
data
● Collecting data
Feature Engineering and
Labelling
● Standardization
● Smooth Labels
● Change Labels

7. Trader
● Key idea:
Algorithmic trading strategies
can be improved with signals
from predictions.
● Crossover Moving Average
Strategy example

10. Future work:
● Adding new sources of information
● Improving more advanced
algorithmic trading strategies with
our signals
● Real time trading (with virtual
money)

11. Q&A

12. Thank you
SmartCat
www.smartcat.io
@SmartCat_io